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triton-ascend-case-matmul-large-k

Split the K dimension across the grid with atomic_add for large-K matrix multiplication.

258|48|Updated Jun 22, 2020
Search Tags:#parallelism#triton#ascend#matmul#kernel-optimization#split-k#tl.atomic_add
One-click install
npx skills add https://github.com/mindspore-ai/akg --skill triton-ascend-case-matmul-large-k
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: triton-ascend-case-matmul-large-k
Source: https://github.com/mindspore-ai/akg/tree/main/akg_agents/python/akg_agents/op/resources/skills/triton-ascend/cases/triton-ascend-case-matmul-large-k
Command: npx skills add https://github.com/mindspore-ai/akg --skill triton-ascend-case-matmul-large-k

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Optimizes large-K matrix multiplication by splitting the K dimension to keep all cores busy, reducing global synchronization and improving throughput for cases where K >> M and N.

Core Features & Use Cases

  • Split-K parallelization across grid dimensions to maximize core utilization and throughput.
  • Atomic-add accumulation of partial results to avoid costly barriers.
  • Workspace-based reduction option to minimize kernel-wide synchronization.
  • Target scenario: M and N around 256 with K around 131072, where traditional matmul stalls.

Quick Start

Run the matmul_large_k example with M=256, K=131072, N=256 to observe Split-K parallelism gains.

Frequently Asked Questions about triton-ascend-case-matmul-large-k

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize large-K matrix multiplication when K is much larger than M and N?▼

Large-K matrix multiplication is optimized by splitting the K dimension across grid blocks and using atomic_add to accumulate partial results, keeping all cores busy to reduce global synchronization stalls.

What is Split-K parallelism and how does it improve matmul kernel performance?▼

Split-K parallelism divides the K dimension across the grid using a matmul_splitk_kernel pattern, maximizing core utilization and throughput for matrix multiplication where K significantly exceeds M and N.

How do I run a Split-K matmul kernel on Ascend for matrices with K=131072?▼

Run the matmul_large_k example configured with M=256, K=131072, and N=256, adjusting BLOCK_M, BLOCK_N, and SPLIT_K variables to maximize core utilization and observe Split-K parallelism gains.

Can I use Triton atomic_add to accumulate partial matmul results on Ascend hardware?▼

Triton atomic_add accumulates partial matmul results across the grid without costly global synchronization barriers, and an optional workspace-based reduction further minimizes kernel-wide synchronization overhead.

Why does traditional matmul stall when K is very large and how do I avoid it?▼

Traditional matmul stalls because large K dimensions leave cores underutilized during sequential accumulation; splitting K across the grid with configurable SPLIT_K avoids this bottleneck and improves runtime performance.

Do I need a workspace-based reduction to avoid global synchronization in Split-K matmul?▼

A workspace-based reduction is an optional method to minimize kernel-wide synchronization in Split-K matmul, complementing the default tl.atomic_add accumulation to optimize throughput on Ascend devices.

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